How to best convey diverse data,
Draw the graph you want with data visualization .

Learning 4 Deep Learning Project Curriculum

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Introduction

No matter what research we conduct or what results we produce, the results are usually expressed in numbers. And the best way to communicate this is through visualization. There are graphs appropriate for each data characteristic, and the ability to create a variety of graphs is the best way to present the results you've worked so hard to produce.

This course covers how to appropriately visualize a variety of data, from the very basics through the creation of real-world papers and examples. Beyond simply using a simple API, the course delves into how to customize the detailed elements of a graph. This allows students to create any graph they desire, provided they have the data.

I am confident that not only the lectures but also the lecture materials will serve as excellent reference materials for you to use in future presentations or papers.

Characteristics

This course consists of seven chapters, with Chapter 1 being the most distinctive. In this chapter, you'll learn the fundamentals applicable to a wide range of Matplotlib graphs, establishing a solid foundation for visualization. It covers all the elements shown in the Matplotlib anatomy section on the official Matplotlib website, and you'll be able to apply them throughout the remaining chapters, providing a foundational understanding not found in other courses.

Chapters 2 through 7 cover how to draw a variety of graphs. Here's what you'll learn:

• Chap.2 Line Plot
• Chap.3 Scatter Plot
• Chap.4 Bar Plot
• Chap.5 Histogram
• Chap.6 3D and Contour Plots
• Chap.7 Other Plots
  • Imshow
  • Stem Plot
  • Box and Whisker Plot
  • Pie Plot
  • Vector Field

By mastering the above content, students will be able to handle most of the visualizations used in practice.

Visualization Examples

Here are some actual visualizations we create in class: